Intelligent building monitoring

ABSTRACT

A method includes receiving data characterizing a time-dependent first sensor data detected by a first sensor, a time-dependent second sensor data detected by a second sensor, a time-dependent third sensor data detected by a third sensor, a first set of threshold values associated with the first sensor, a second set of threshold values associated with the second sensor, and a third set of threshold values associated with the third sensor and a time window. The first, second, and third sensors are located in a first space of a building. The method further includes calculating a first performance index, a second performance index, and a third performance index. The method also includes classifying the first performance index, the second performance index, and the third performance index into one of a plurality of performance indicators. The method further includes assigning a performance rating score for a space based on the classification.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.63/333,690, filed on Apr. 22, 2022, entitled “INTELLIGENT BUILDINGMONITORING,” which is hereby incorporated by reference in its entirety.This application also claims the benefit of U.S. Provisional ApplicationNo. 63/384,225, filed on Nov. 17, 2022, entitled “INTELLIGENT BUILDINGMONITORING,” which is hereby also incorporated by reference in itsentirety.

TECHNICAL FIELD

The present disclosure is directed to building environment sensorsystems.

BACKGROUND

Indoor air factors of a building (e.g., carbon-dioxide or CO₂concentration, fine particulate matter or PM_(2.5) concentration, etc.)can impact the health and wellbeing of the occupants of the building.Monitoring the building can help in identifying problems associated withthe building, and determining the corresponding solutions. This can bedone by installing various sensors (e.g., CO₂ sensors, PM_(2.5) sensors,etc.) that can make real-time spot measurements of building conditions.The real-time spot measurements can assist with assessing the airconditions of the building, provided that the raw data are combined withadditional information to aid in the interpretation of those data. Someconventional systems that utilize multiple sensors to determinereal-time indoor air quality are challenged by the large amount ofsensor data and thus often use techniques based on one-time spotmeasurements, which are unable to determine and identify long-termtrends in building air quality.

SUMMARY

Various aspects of the disclosed subject matter may provide one or moreof the following capabilities.

A method includes receiving data characterizing a time-dependent firstsensor data detected by a first sensor, a time-dependent second sensordata detected by a second sensor, a first threshold value associatedwith the first sensor, a second threshold value associated with thesecond sensor and a time window. The first sensor and the second sensorare located in a first space of a building. The method further includescalculating a first performance index based on the first sensor data andthe time window and a second performance index based on the secondsensor data and the time window. The method also includes classifyingthe first performance index and the second performance index into one ofa plurality of performance indicators wherein the classification of thefirst performance index and the second performance index is based oncomparison of the first performance index and the second performanceindex with the first threshold value and the second threshold value,respectively. The method further includes determining a performancerating score for the first space by scoring the classification of firstperformance index and the second performance index within the pluralityof performance indicators.

One or more of the following features can be included in any feasiblecombination.

In some implementations, calculating the first performance indexincludes selecting a first portion of the time-dependent first sensordata that temporally spans from a first time to a second time. Thedifference between the second time and the first time corresponds to thetime window. The method also includes calculating the first performanceindex by averaging the first portion of the time-dependent first sensordata.

In some implementations, the method further includes calculating a thirdperformance index. The calculating includes selecting a second portionof the time-dependent first sensor data that temporally spans from athird time to a fourth time. The difference between the fourth time andthe third time corresponds to the time window. The method also includescalculating the third performance index by averaging the second portionof the time-dependent first sensor data. In some implementations, themethod further includes classifying the first performance index to afirst category of the plurality of categories, and classifying the thirdperformance index to a second category of the plurality of categories.The method may include classifying the first performance index to afirst category when the first performance index is greater than thefirst threshold value associated with the first sensor. The method mayinclude classifying the second performance index to a second categorywhen the second performance index is smaller than the first thresholdvalue.

In some implementations, the method further includes rendering, in agraphical user interface, a first visual representation of the firstperformance indicator and a second visual representation of the secondperformance indicator; generating a first graphical object indicative ofthe first performance index; generating a second graphical objectindicative of the third performance index; and rendering, in thegraphical user interface, the first graphical object over the firstvisual representation and the second graphical object over the secondvisual representation. In some implementations, the first graphicalobject is rendered in a first region of the graphical user interface ata first time, wherein the first graphical object traverses from thefirst region of the graphical region to the first visual representationduring a time period subsequent to the first time. In someimplementations, the method further includes rendering, in a graphicaluser interface, a visual representation of at least one of the firstperformance indicator, the second performance indicator, and theperformance rating score of the first space for a first time period; andrendering in a graphical user interface a second visual representationof at least one of the first performance indicator, the secondperformance indicator, and the performance rating score of the firstspace for a second time period.

In some implementations, the method further includes receivingenvironmental data including at least one of ventilation, infiltrationand recirculation rates, heating filter type, airflow, space dimensions,and floor plans. The method may also include the step of determining afirst sensor position for a first sensor and a second sensor positionfor a second sensor within the first space of the building.

In some implementations, the method further includes receiving datacharacterizing a time-dependent third sensor data detected by a thirdsensor, and a set of third threshold values associated with the thirdsensor, wherein the third sensor is located in the first space of thebuilding, calculating a third performance index based on the thirdsensor data and the time window, classifying the third performance indexinto one of a plurality of performance indicators where theclassification of the third performance index is based on a comparisonof the third performance index with the third set of threshold values,and wherein determining the performance rating score for the first spacefurther comprises scoring the classification of the third performanceindex within the plurality of performance indicators.

Non-transitory computer program products (i.e., physically embodiedcomputer program products) are also described that store instructions,which when executed by one or more data processors of one or morecomputing systems, causes at least one data processor to performoperations herein. Similarly, computer systems are also described thatmay include one or more data processors and memory coupled to the one ormore data processors. The memory may temporarily or permanently storeinstructions that cause at least one processor to perform one or more ofthe operations described herein. In addition, methods can be implementedby one or more data processors either within a single computing systemor distributed among two or more computing systems. Such computingsystems can be connected and can exchange data and/or commands or otherinstructions or the like via one or more connections, including aconnection over a network (e.g. the Internet, a wireless wide areanetwork, a local area network, a wide area network, a wired network, orthe like), via a direct connection between one or more of the multiplecomputing systems, etc.

These and other capabilities of the disclosed subject matter will bemore fully understood after a review of the following figures, detaileddescription, and claims.

BRIEF DESCRIPTION OF THE FIGURES

These and other features will be more readily understood from thefollowing detailed description taken in conjunction with theaccompanying drawings, in which:

FIG. 1 is a flow chart of an exemplary method for assigning a healthperformance indicator category to a space in a building;

FIG. 2 illustrates an exemplary graphical user interface for displayingthe distribution of performance indices associated with a space in abuilding;

FIG. 3 illustrates an exemplary spatial monitoring system for monitoringindoor air factors of a space; and

FIG. 4 illustrates a block diagram illustrating an example of acomputing system, in accordance with some example embodiments.

FIG. 5 illustrates an exemplary graphical user interface for displayingthe distribution of performance indices associated with a space in abuilding;

FIG. 6 illustrates an exemplary graphical user interface for displayingthe distribution of performance indices associated with a space (i.e., afloor) in a building;

FIG. 7 illustrates an exemplary graphical user interface for displayingthe distribution of data associated with sensors associated with a spacein a building;

FIG. 8 illustrates an exemplary graphical user interface for displayingthe distribution of data associated with sensors associated with a spacein a building;

FIG. 9 illustrates an exemplary graphical user interface for displayingthe distribution of data associated with sensors associated with a spacein a building;

FIG. 10 illustrates an exemplary graphical user interface for displayingthe distribution of data associated with sensors associated with a spacein a building;

FIG. 11 illustrates an exemplary graphical user interface for displayingthe distribution of data over time associated with sensors associatedwith a space in a building;

FIG. 12 illustrates an exemplary graphical user interface for displayingthe distribution of data over time associated with sensors associatedwith a space (i.e., a floor) in a building;

FIG. 13 illustrates an exemplary graphical user interface for displayingthe distribution of data for two parameters obtained from sensorsassociated with a space in a building;

FIG. 14 illustrates an exemplary graphical user interface for displayingthe distribution of data for two parameters obtained from sensorsassociated with a space (i.e., a floor) in a building;

FIG. 15 illustrates an exemplary graphical user interface for displayingthe distribution of a parameter over time associated with sensorsassociated with a space in a building;

FIG. 16 illustrates an exemplary graphical user interface for displayingthe distribution of a parameter over time associated with sensorsassociated with a space in a building;

FIG. 17 illustrates an exemplary graphical user interface for displayingthe distribution of a parameter over time associated with sensorsassociated with a space (i.e., a floor) in a building; and

FIG. 18 illustrates an exemplary graphical user interface for displayingdata quality.

FIG. 19 also illustrates an exemplary graphical user interface fordisplaying data quality.

FIG. 20 illustrates an exemplary graphical user interface used fordetecting sensor issues.

FIG. 21 illustrates an exemplary graphical user interface used fordetecting a floor issue.

FIG. 22 illustrates an exemplary graphical user interface used fordetecting a building issue.

DETAILED DESCRIPTION

Indoor air quality (IAQ) of a building can impact the health andwell-being of occupants of the building. The IAQ can depend on variousindoor air factors associated with the building (e.g., CO₂concentration, PM_(2.5) concentration, temperature, humidity,concentration of volatile organic compounds (VOCs), radon concentration,etc.). These factors can be measured by installing sensors at variouslocations of the building. The sensors can measure the concentration ofvarious factors that can then be monitored (e.g., to determine if theconcentration is in an undesirable range). Additionally, it can bedesirable to monitor the IAQ in real-time (e.g., throughout the day) inorder to detect changes in the air quality of the building (e.g.,time-dependent deterioration in the air quality of a building or a spacetherein).

Monitoring IAQ that depends on multiple indoor air factors (measured bymultiple sensors) in real-time can be challenging (e.g., due to largeamount of sensor data). In some implementations of the current subjectmatter, systems and methods for calculating a performance indicator of abuilding are described that provide an accurate description of theindoor air quality of the building based on comparing the instantaneousspot measurement from the sensor with predetermined thresholds (e.g.,published thresholds). Additionally, by using predetermined thresholds,some implementations of the current subject matter may providecomputational advantages over conventional systems that are inadequatelyequipped to process a large amount of sensor data. These approaches canaccount for several critical factors in determining human health risk,and therefore the overall rating or performance of the space andbuilding. For example, these approaches can account for appropriateaveraging times for each sensor and IAQ parameter, health-basedthresholds based on those averaging times, a method for placing one ormore sensors within a space, a method for determining whether any IAQdata points or datasets are unreliable, a method for scoring theperformance of the space across the various IAQ parameters using theaveraging times and health-based thresholds, a method for scoring theperformance of the building across multiple sensors, and a method forusing blockchain technology to create an immutable record for eachparameter (including averaging time and threshold), for each space, andfor the building, etc.

A building can be partitioned into multiple spaces. A space may becomposed of a room, a collection of rooms, a floor, a whole building, orany portion thereof. Each space can include one or multiple sensors(e.g., CO₂ sensor, PM_(2.5) sensor, humidity sensor, temperature sensor,volatile organic compound (VOC) sensor, etc.) that can monitor the airquality of the space. A sensor package composed of multiple sensors canbe located within a space. A plurality of sensor packages each composedof multiple sensors can be located within a space.

Based on sensor data in a given space, a spatial performance indicatorcan be assigned to the space. The building health performance indicatorcan be assigned based on the spatial performance indicators associatedwith the different spaces in the building. Determining the buildinghealth performance indicator from spatial performance indicators canallow for determining spatial patterns in the indoor air quality of thebuilding. Additionally, each spatial performance indicator can becalculated at various time instances (e.g., by averaging sensor dataover a time window). This can allow for determining temporal patterns inthe indoor air quality of individual spaces and the building.

Spatial and building health performance indicators can provide buildingoperators actionable information to improve the comfort, health and workperformance of occupants of a building. Unlike existing techniques formonitoring indoor air quality of buildings which may rely on techniquesbased on one-time spot measurement, spatial and building healthperformance indicators can help to identify spatial and temporalpatterns in the air quality which can provide clues to the sources ofair quality issues in the building. Real-time measurements may providebuilding operators with actionable information to improve undesirable(e.g., suboptimal) building conditions (e.g., even before occupants areaffected). Moreover, real-time measurements help can building operatorsensure that buildings are supporting occupant comfort, health, safety,and work performance which can boost employers' bottom lines throughreducing employee absenteeism and improving productivity.

In some embodiments, a processor for a monitoring system may receivedata from the placed sensors including data for determining performanceindicators.

FIG. 1 is a flow chart of an exemplary method for assigning aperformance indicator to a space in a building (or a spatial performanceindicator). The spatial performance indicator can be indicative of theindoor air quality (IAQ) of the space. At step 102, data characterizinga time-dependent first sensor data detected by a first sensor (e.g., CO₂sensor), and a time-dependent second sensor data detected by a secondsensor (e.g., PM_(2.5) sensor) can be received. The data can be receivedby a monitoring system configured to monitor the indoor air quality ofthe building. One or more sensors (e.g., first sensor, second sensor,etc.) or sensor packages (which contain sensors that measure various IAQparameters packaged together in one device) can be positioned in a spaceof the building (e.g. a first space) to detect indoor air factors.

The raw sensor data from all sensors in a space can be combined withthreshold values associated with the various sensor data (e.g., firstsensor data, second sensor data, etc.) and a time window (e.g.,averaging time). Therefore, each parameter in each sensor can beassigned a performance index based on the raw data combined with acomputed score based on the averaging time and thresholds. Theperformance index values across IAQ parameters in a space are thenscored to give a performance rating scores for the space for a suitabletime window (e.g., day, week, month, year, and/or for all-time). Theperformance rating scores for each space in a building are then used todetermine a building performance indicator for a suitable time window(e.g., that day, week, month, year, and/or for all-time).

In some embodiments, the systems and methods described herein mayinclude a method for determining whether any sensor data (e.g.,associated with different sensors) received at step 102 is unreliable.For example, in some embodiments, a method may perform data qualitychecks on IAQ datasets prior to assigning performance indicators. Dataquality checks may include, but are not limited to, assessments of datacompleteness, outliers, and variability. For example, in someembodiments, assessments of data completeness may compare the amount ofdata collected by one or more sensors to an amount of data expected forindividual parameters. Data completeness assessments may be performedfor individual parameters and for all parameters combined. In someembodiments, data completeness assessments may also identify and removeduplicates in datasets. In some embodiments, data completenessassessments may also identify gaps in the data. Additionally,assessments of outliers may identify any measurements outside of sensormeasuring ranges or any outside of expected ranges of values.Assessments of variability may include identification of periods whenthe variability in sensor measurements is lower than would be expectedin a building under normal operating conditions.

Data quality checks may include one or more checks to determine datacompleteness. For example, the data set may be analyzed to determine ifthere are prolonged gaps in the data from any sensors or if the data isrelatively complete overall. In this manner, the use of incomplete datasets, which may result in biased or unrepresentative analysis, isprevented. In some embodiments, data completeness may be determined on atimescale (i.e., 1 hour), and represented as a percentage of the sensorsfor a parameter which reported data at least once during the timescalefor the time period. Data quality checks may also screen for duplicates.Duplicates may be indicative of problems with data recording ortransmission. The inclusion of duplicates in the dataset may have led tomisleading visual or statistical summaries, which can be avoided byperforming data quality checks as described herein.

Data quality checks may also screen for sensors with data gaps for allparameters. Data gaps may be indicative of sensor malfunction, unstablepower, and/or poor internet connectivity. Data gaps for larger than 10%of the time period may be flagged for intervention. In some embodiments,sensors with data gaps for individual parameters may be flagged. Forexample, in some embodiments, less than 10% of values should be reportedas Not Applicable so that data quality is preserved and the dataset isrepresentative of the monitored areas.

Data quality checks may also screen for sensors reporting values thatfall outside of measurements ranges. These values may have reducedaccuracy or may be prone to error. If a sensor frequently reportsmeasurements outside its measurement range, the sensor may be flaggedfor calibration or replacement. Values may be considered outside thesensor measurement range if they are less than the sensor measurementrange minimum or greater than or equal to the range maximum. Data fromsensors with more than 1% of data outside the sensor measurement rangesin the time period may be represented visually. Data quality checks mayalso screen for sensors that report abnormally low variation. Ingeneral, indoor environmental quality parameters may vary naturallythroughout the course of a day. In cases where specific parameters haveabnormally low variation over the course of several days, a sensor maybe malfunctioning thus resulting in low data quality.

Data quality checks may be associated with follow-up actions such asconfirming that sensor power and connectivity are stable, confirmingthat sensors are properly calibrated, confirming that sensors areappropriately responding to changes in environmental conditions,confirming that the sensors are functional, confirming that connectivityfor the sensors is functional, and the like.

At step 104, performance indices can be calculated for sensor data(e.g., associated with different sensors) received at step 102. Forexample, a first performance index can be calculated for the firstsensor data and a second performance index can be calculated for thesecond sensor data. Determination of a performance index for the firstsensor data can be based on selecting a portion of sensor data andcalculating an index (e.g., based on the averaging time) associated withthe selected sensor data. In some implementations, a first portion ofthe first sensor data that temporally spans from a first time to asecond time (e.g., the first portion of the first sensor data wasdetected between the first time and the second time) can be selected.The duration of the first portion of the first sensor data correspondsto the time window (e.g., difference between the second time and thefirst time is the duration of the time window). Once the first portionof the first sensor data has been selected, a first performance indexcan be calculated by, for example, calculating an average of firstportion of the first sensor data. These performance indices arecontinually computed, such that ‘rolling averages’ are calculated foreach IAQ parameter in each space. In some embodiments, ‘rollingaverages’ for each parameter may include various time windows. In someembodiments, ‘rolling averages’ for each parameter may include the sametime window.

This process can be repeated for sensor data generated by differentsensors in the first space of the building. For example, a secondperformance index can be calculated for a portion of a second sensordata generated by the second sensor located in the same space as thefirst sensor. The second sensor performance index can be calculated byselecting a portion of the second sensor data (e.g., based on the timewindow), and by calculating an average of the selected portion of thesecond sensor data.

In some implementations, multiple performance indices can be calculatedfor sensor data generated by a sensor. For example, a second portion ofthe first sensor data that temporally spans from a third time to afourth time (e.g., the second portion of the first sensor data wasdetected between the third time and the fourth time) can be selected.The duration of the second portion of the first sensor data correspondsto the time window (e.g., difference between the fourth time and thethird time is the duration of the time window). A performance index ofthe second portion of the first sensor data can be calculated by, forexample, calculating an average of second portion of the first sensordata.

In some implementations, different portions of a sensor data (e.g.,first portion and second portion of the first sensor data) can betemporally staggered. For example, the temporal extent of the firstportion of the first sensor data may not overlap with the temporalextent of the second portion of the first sensor data. In someimplementations, different portions of the sensor data used to calculateperformance indices may temporally overlap.

For a given space in a building, multiple performance indices can begenerated from different portions of sensor data from a sensor in thegiven space and/or from sensor data generated by different sensors inthe given space. At step 106, the various performance indices calculatedat step 104 can be classified into one of a plurality of categories(e.g., health optimized, excellent, action, alert, limit, etc.). Theclassification of the performance indices (e.g., first performanceindex, second performance index, etc.) is based on threshold valuesassociated with each IAQ parameter. For example, sensor data from afirst sensor (e.g., CO₂ sensor) can be associated with a first set ofthreshold values, and a second sensor (e.g., PM_(2.5) sensor) can beassociated with a second set of threshold values, and, when combined, anoverall category is determined for the space.

A performance index can be classified into one of a plurality ofperformance indicators based on comparison of the performance index withthe corresponding set of threshold values (e.g. threshold valuesassociated with the sensor data from which the performance index isgenerated). In some implementations, the classifications for performanceindices can include (e.g., in decreasing order of desirability) healthoptimized, excellent, action, alert and limit. For example, aperformance index can be classified as a second performance indicator(e.g., excellent) if it has a value between a first threshold value anda second threshold value, can be classified as a third performanceindicator (e.g., action) if it has a value between a second thresholdvalue and a third threshold value, etc. In some implementations, theperformance index can be classified as a given performance indicatorbased on a single comparison with a threshold value. For example, aperformance index can be classified as health optimized if theperformance index has a value below a first threshold value. Table 1below provides exemplary threshold values for sensors configured todetect CO₂, PM_(2.5), total volatile organic compounds (TVOC), radon,temperature/relative humidity (RH), and noise, respectively. Based onthe threshold values, the performance index can be classified as havinga performance indicator corresponding to one of health optimized,excellent, action, alert and limit.

The threshold values for the performance indices may be for IAQparameters for health, including but not limited to, CO₂, TVOCs,PM_(2.5), and/or radon. Additionally, threshold values for thecategorization of performance indices may be for thermal parameters.Thermal parameters may include, but are not limited, to temperature andRH. Additional threshold values for the categorization of performanceindices may be for noise. Threshold values for parameters may vary byapplication. For example, threshold values for commercial officeapplications may be different than for residential applications or forindustrial applications.

For example, Table 1 illustrates threshold values for a commercialoffice application. As shown, threshold values may be provided for avariety of performance indices including CO₂, PM_(2.5), TVOC, radontemperature/relative humidity, and noise. Relative humidity may bemeasured based on RH_(out−x), where x=1, 6, or 11, which is x less thanthe RH that a parcel of outdoor air would have if its temperature waschanged from the outdoor temperature at a given time to the indoortemperature at the same time without changing the water content of theair.

TABLE 1 CO₂ PM_(2.5) TVOC Radon Temperature Noise (ppm) (ug/m³) (ppb)(pCi/L) (F)/RH (%) (dBA) Health <800 <5 <300 <0.4 70 < Temp < 76 & <60optimized 30 < RH < 60 Excellent <1000 <15 <1000 <1.3 68 < Temp ≤ 70 or<70 76 ≤ Temp < 78 & RH_(out-1) < RH ≤ 30 or 60 ≤ RH < 65 Action <1500<35 <2000 <2 67 < Temp ≤ 68 or 78 ≤ <75 Temp < 80 & RH_(out-6) < RH ≤RH_(out-1) or 65 ≤ RH < 70 Alert <2500 <50 <3000 <4 66 < Temp ≤ 67 or 80≤ <80 Temp < 82 & RH_(out-11) < RH ≤ RH_(out-6) or 70 ≤ RH < 80 Limit≥2500 ≥50 ≥3000 ≥4 Temp ≤ 66 or Temp ≥ ≥80 82 & ≤ RH_(out-11) RH ≥ 80

In some embodiments, in addition to classifying performance indices intothe different performance indices (e.g., from different sensors), eachsensor in a given space can be assigned a performance rating score.

For example, if at least 85% of the computed indices over a certainperiod of time fall within the top two categories (i.e., healthoptimized and excellent), and at least 50% of the computed indices arein the top category (i.e., health optimized), the space corresponding tothe different sensors may have a performance rating score of “healthoptimized.”

In another example, if at least 85% of the computed indices over acertain period of time fall within the top two categories, and less than50% of the computed indices are in the top category (health optimized),the space may have a performance rating score of “excellent”.

In yet another example, if at least 75% but less than 85% of thecomputed indices over a certain period of time are in the top twocategories, the space may have a performance rating score of“excellent”.

In another example, if less than 75% of the computed indices over acertain period of time are in the top two categories, the space may havea performance rating score of “conventional”.

In this manner, the disclosed systems and methods may score spaceswithin building.

In addition, each space may be tagged with the number of indices thatfall into the ‘action’, ‘alert’, and ‘limit’ categories over a certainperiod of time. For example, a space might be labeled “excellent” withtwo “actions” for the week, as a means of notifying the buildingowner/operator that the space is performing well, but there are specificareas and times that might need attention and corrective action.

In some embodiments, the performance rating score may be composed of anynumber of scores. For example, the performance rating score may becomposed of a first score and a second score and a third score. Thefirst score of the performance rating score may correspond to IAQparameters associated with heath that are determined separately from asecond score that corresponds to one or more thermal parameters. Thethird score may also be determined separately and may correspond tonoise. For example, a first score corresponding to IAQ parametersassociated with health may be based on a score computed based on athresholding of IAQ parameters including, but not necessarily limitedto, CO₂, TVOCs, PM_(2.5), and/or radon. For example, a second scorecorresponding to temperature and RH may be determined based on athresholding of temperature and RH together. For example, a third scorecorresponding to noise may be determined based on a thresholding ofnoise.

At step 108, performance rating score can be assigned to a space in thebuilding (e.g., the first space that includes the first sensor and thesecond sensor). The assignment can be based on the determinedperformance rating scores of the sensors in the space. As describedabove, the classification of the sensors in the space is based on theclassification of performance indices associated with the sensors (e.g.,first performance index, second performance index, third performanceindex etc.) performed at step 106. In some implementations, the space isassigned the performance rating score corresponding to the worstperforming sensor in the space. For example, if the first sensor in thespace is scored as action at a specific time and the second sensor isscored as alert at the same time, the space may be assigned aperformance rating score of alert.

In some implementations, the performance rating score can be assigned tothe space (e.g., the first space) based on the distribution of theperformance indicators assigned to the performance indices from sensordata generated by sensors.

In some implementations, a building health performance indicator can bedetermined based on the performance indicators or performance ratingscores assigned to spaces in the building. For example, a building mayobtain a building health performance indicator ranging from optimized,excellent, action, alert, or limit, based on the percentage of spacesreceiving performance rating scores of same classification. Buildingsmay be scored in an analogous manner to how individual sensors arescored. For example, the raw data may be used to generate rolling ortime-weighted averages which are then classified into bins. In someembodiments a building score may be assigned based on how all therolling or time-weighted averages from all the sensors in the buildingcompare to the thresholds using the same cutoffs used for an individualsensor. In some embodiments, some sensors in the building may beweighted differently from other sensors in the building.

At step 110, the performance indicator(s) and performance rating scoreassigned to the first space can be provided. For example, theperformance indicator assigned to the first space can be presented in agraphical user interface (GUI), such as FIG. 2 . Performance indicatorsmay also be presented in visualizations including, but not limited to,boxplots, timeseries plots, or scatterplots each of which may begenerated for a sensor, floor, or building. In some embodiments,performance indicator(s) and performance rating scores can be providedto a user via an application programming interface (API) or the like.Visualizations may be incorporated into reports or displays.

At step 112, the performance indicator(s) and performance rating scorescan be used for adjusting sensor placement within the space. Forexample, data from the performance indicator(s) and performance ratingscores can be incorporated into model in order to direct follow upactions after sensor data from a space indicate that the space has aperformance rating score indicative of exceeding action, alert, or limitthresholds. In some embodiments, a model may be used to check whetherthe current sensor density is sufficient to determine whether theexceedance is locally contained and, if it is not sufficient, the modelcould also be used to recommend where additional sensors be placed inthe space. In cases where it is confirmed that sensor density issufficient, comparison of data from the sensor with the exceedance withdata from nearby sensors can help determine follow-up actions (e.g., ifthe sensor needs replaced because it was malfunctioning, if there arespatial or temporal patterns indicating how IAQ improvements could beachieved).

In some embodiments, data characterizing a time-dependent first sensordata detected by a first sensor, a time-dependent second sensor datadetected by a second sensor, and a time-dependent third sensor datadetected by a third sensor may be received. Additionally, datacharacterizing a set of first threshold values associated with the firstsensor, a set of second threshold values associated with the secondsensor, and a set of third threshold values associated with the thirdsensor may also be received. Data characterizing a time window may bereceived. In some embodiments, the first sensor, the second sensor, andthe third sensor are located in a first space of a building. A firstperformance index can be calculated based on the first sensor data andthe time window, a second performance index can be calculated based onthe second sensor data and the time window, and a third performanceindex can be calculated based on the third sensor data and the timewindow. Each of the first performance index, the second performanceindex and the third performance index can be classified into one of aplurality of performance indicators, where the classification of thefirst performance index, the second performance index, and the thirdperformance index is based on comparison of the first performance index,the second performance index, and the third performance index with thefirst set of threshold values, the second set of threshold values, andthe third set of threshold values respectively. A performance ratingscore can be determined for the first space by scoring theclassification of first performance index, the second performance index,and the third performance index within the plurality of performanceindicators. The performance rating score assigned to the first space canthen be provided or displayed in a graphical user interface, applicationuser interface, report or the like.

FIG. 2 illustrates an exemplary graphical user interface 200 fordisplaying the distribution of performance indices (associated withsensor data from a space) over the various performance indicators. Othervisualizations including unique boxplots, timeseries plots, or scatterplots may also be used to visualize performance indices. Healthperformance indicators (which are representative of sensor data averagedover one or more averaging times/time windows) are displayed in the farleft image 200. Raw indoor environmental quality data can be collectedby sensors in a building and aggregated into health performanceindicators in time-weighted averages (e.g., 1-hour and 8-hour averages)covering the building's occupied hours as displayed in the far leftimage 200. These health performance indicators can be sorted into theappropriate bins and response categories (middle image). Sorting thehealth performance indicators may be based on comparing the averagesagainst parameter-specific thresholds. Based on the sorting, the scoringalgorithm can compute a performance rating score for the space (rightimage), and then a score for the entire building (e.g., a buildingperformance indicator). For example, the scoring algorithms may providea building's indoor air quality, thermal, and/or noise scores, as wellas action, alert and limit notifications.

Each health performance indicator can be represented by a graphicalobject (e.g., by an image of a circle, marble, or ball), and theperformance indicators are then visually represented being sorted intodifferent categories (e.g., a bin, or graphical depiction of a floor ofa building). Depending upon the classification of the performance index,the graphical object associated with the performance index is placed inone of the five bins 202-210. For example, a health optimizedperformance indicator is represented by a first bin 202, an excellentperformance indicator is represented by a second bin 204, an actionperformance indicator is represented by a third bin 206, an alertperformance indicator is represented by a fourth bin 208, and a limitperformance indicator is represented by a fifth bin 210.

In some implementations, the rendering of the graphical objectsassociated with performance indices in the GUI 200 can be dynamic. Forexample, a graphical object can first appear in a first region 200 ofthe GUI 200 and then traverse from the first region to one of the fivebins 202-210. After the graphical object arrives in a given bin, it maycontinue to be subsequently displayed. With the passage of time as moresensor data is collected, new graphical objects (e.g., corresponding tonew performance indices) are generated and placed in one of the bins202-210.

In some implementations, a graphical user interface may be configured todisplay and render graphical objects associated with performance indicesor performance rating scores over various time frames (e.g., all-time,past month, past week, past day).

Graphical objects may be included in a graphical user interface,integrated into an application programming interface, or used togenerate reports that may be provided to a user in any suitable form(e.g., document reports, graphical display).

FIG. 3 illustrates an exemplary spatial monitoring system for monitoringindoor air factors of a space. The spatial monitoring system can includesensors 302-306 that can detect air factors in the space.

Embodiments of the present disclosure may include a method ofpositioning the sensors 302-306 within a spatial monitoring system. Insome embodiments, one or more sensors 302-306 or sensor packages may bedistributed within a space at various locations. For real-time IAQ datato adequately characterize an indoor space, a sufficient number ofsensors must be placed at appropriate locations within large open indoorspaces (e.g., open offices, large meeting rooms, cafeterias, etc.). Amodel may receive environment data and generate recommendations for thenumber of required sensors, sensor types, and the location of theirplacement within the space. Environment data may include informationabout specific air parameters of interest (e.g., CO₂, PM_(2.5)).Environment data may include information about the space where sensorswill be placed (e.g., ventilation, infiltration, and recirculationrates; heating, ventilation and air conditioning (HVAC) filter type;airflow within the space; and the space dimensions). In someembodiments, if environmental data corresponding to the characteristicsof the space are unknown, default parameters for the environmental datamay be used. Additionally, the environment data may include floor plansfor the space.

The model may be used to determine appropriate positions for a sensor orsensor package within a space. Using received environmental data, themodel may determine a zone of possible placements for each sensor orsensor package. The zone of possible placements may be based on acalculation how far away a sensor could be placed from a source of theair parameter of interest while still being able to measure elevatedparameter levels at a certain timepoint or ever (i.e., before it isremoved/diluted). Although source concentrations and elevated targetlevels measured by the sensor can be customized, in some embodiments themethod may use default parameters corresponding to the limit thresholdconcentrations for each sensor as the source concentrations and theaction threshold concentrations for each air parameter of interest toindicate an elevated concentration.

In some embodiments, the model may recommend that sensors be placedthroughout a space such that a sustained source at a concentration equalto the limit threshold within the area covered by each sensor would bedetected (i.e., would reach an action threshold) by a sensor within acertain amount of time.

In some embodiments, the model may also be used to direct follow upactions after sensor data from a space has exceeded action, alert, orlimit thresholds. In such an embodiment, the model can be used to checkwhether the current sensor density is sufficient to determine whetherthe exceedance is locally contained and, if it is not sufficient, themodel could also be used to recommend where additional sensors be placedin the space. In cases where it is confirmed that sensor density issufficient, comparison of data from the sensor with the exceedance withdata from nearby sensors can help determine follow-up actions. Examplesof follow-up actions may include recommendations that a sensor needsreplacement, or adjustments in positioning. Other examples of follow-upactions include spatial or temporal patterns which indicate how indoorair quality improvements could be achieved.

The sensors 302-306 can transmit sensor data representative of thedetected air factors to a performance index calculating unit 310 thatcan calculate performance indices associated with the sensor data (e.g.,as described in step 104 of FIG. 1 ). The performance indices generatedby the performance index calculating unit 310 can be provided to theperformance index classification unit 312 that can classify theperformance indices as different performance indicators (e.g., asdescribed in step 106 of FIG. 1 ). The classified performance indicescan be provided to the scoring unit 314 that can assign performanceindicators and space performance rating scores to the space (e.g., asdescribed in step 108). In some implementations, information associatedwith one or more of the health performance indicators of the space,health performance indices, and health performance indicators associatedwith the performance indices can be provided to an output unit 316. Theoutput unit 316 can display the aforementioned information in agraphical user interface (e.g., graphical user interface 200). In someimplementations, the output unit 316 can transmit or store theinformation in a database. In some embodiments, the output unit 316 mayprovide the information to a model.

In some implementations, information associated with one or more of theperformance indices, health performance indicators, space performancerating scores, and building performance indicators can be stored in ablockchain. For example, a health performance index and thecorresponding health performance indicator can be assigned a block in ablockchain. The block can be associated with a space (e.g., the healthperformance index is calculated from the sensor data generated by thesensor located in the space), a building that includes the space and anaveraging time used to calculate the health performance index. Theblockchain can allow for creation of an immutable record of the varioushealth performance indicators and health performance indices generatedin the spaces of a building. In some implementations, informationassociated with the performance of a building can be shared (e.g.,between owner of the building and insurance companies) by sharing theblockchain.

In some implementations, the aforementioned information (e.g., thehealth performance indicators of the space, health performance indices,health performance indicators associated with the performance indices,etc.) can be provided to a building automation systems (BAS) or buildingmanagement systems (BMS) to instantaneously adjust the building systemsbased on the computed performance indicators for the space and building.After the health performance indicators are created, that dataassociated with the health performance indicator can be provided to theBAS/BMS for a corrective action (e.g., instantaneous corrective action).For example, if high CO₂ is detected and categorized as an ‘alert’, thebuilding would automatically adjust the outdoor air damper to bring inmore outdoor air.

FIG. 4 illustrates an exemplary computing system 400 configured toexecute the data flow described in FIG. 1 . The computing system 400 caninclude a processor 410, a memory 420, a storage device 430, andinput/output devices 440. The processor 410, the memory 420, the storagedevice 430, and the input/output devices 440 can be interconnected via asystem bus 450. The processor 410 is capable of processing instructionsfor execution within the computing system 400. Such executedinstructions can implement one or more steps for calculating andclassifying performance indices, assigning a performance indicator to aspace/building, etc. In some example embodiments, the processor 410 canbe a single-threaded processor. Alternately, the processor 410 can be amulti-threaded processor. The processor 410 is capable of processinginstructions stored in the memory 420 and/or on the storage device 430.

The memory 420 is a computer readable medium such as volatile ornon-volatile that stores information within the computing system 400.The memory 420 can store performance indices, performance indicators,etc. The storage device 430 is capable of providing persistent storagefor the computing system 400. The storage device 430 can be acloud-based storage system (e.g., AWS), floppy disk device, a hard diskdevice, an optical disk device, a tape device, a solid state drive,and/or other suitable persistent storage means. The input/output device440 provides input/output operations for the computing system 400. Insome example embodiments, the input/output device 440 includes akeyboard and/or pointing device. In various implementations, theinput/output device 440 includes a display unit for displaying graphicaluser interfaces. In some implementations, the GUI 300 can be displayedin a display of the input/output device 440.

FIGS. 5-22 provide illustrations of an implementation of the claimedsubject matter, where sensor data, parameters, and metrics derivedtherefrom are provided to a user via report, graphical user interface,application user interface, or the like.

The implementation illustrated in FIGS. 5-22 illustrates a commercialspace embedded with real-time sensors configured to gather IAQ data. IAQparameters including CO₂, PM_(2.5), and TVOCs were determined based onthe gathered IAQ data, and rolling averages for these IAQ parameterswere compared against the threshold ranges to determine an IAQ score.

Key metrics may be determined and displayed to a user in a report,graphical user interface, application user interface and the like.Examples of key metrics include, IAQ Score, TRH Score, Noise Score, % ofCO₂ data in Health Optimized or Excellent threshold ranges, % ofPM_(2.5) data in Health Optimized or Excellent threshold ranges, % ofTVOC data in Health Optimized or Excellent threshold ranges, and thenumber or names of floors with conventional IAQ scores. Key metrics mayalso indicate sensors associated with values that are consistentlyoutside of a target range. For example, key metrics may include alisting of sensors with elevated TVOC values, elevated PM_(2.5) values,elevated temperatures, elevated noise levels, and the like. Datacompleteness, outliers, variability metrics for sensor data may also beprovided. Data sets, key metrics, performance indicators, and the likemay be provided to a user via a user interface.

In some implementations of the claimed subject matter, users may bepresented with sensor data, parameter data, and metrics derivedtherefrom in a graphical user interface that is integrated into a userapplication such as a website, computer program, mobile application, andthe like. The graphical user interface can be configured to beinteractive and dynamic. For example, the graphical user interface canbe configured to receive user input that generates user input data. Theuser input data may indicate a selection from the user as to whatvisualizations they would like to see. For example, a portion of adisplay screen provided to a user may be updated with a graphical userinterface that shows sensor data for one or a set of parameters, onefloor or a collection of floors, for a particular time range, and thelike. A user may navigate through data provided as a summary to viewspecific subgroups of the visualization data. Upon selecting aparticular subgroup of the visualization, the user may be presented witha more detailed level of data corresponding to the selected subgroup ina second visualization. In some embodiments, the user may be able toprovide user input to adjust the timescales and parameters for theperformance index values, performance rating scores, or the like.

FIG. 5 provides an example of an IAQ summary plot that is configured todisplay data to a user. FIG. 5 illustrates an exemplary graphical userinterface 500 for displaying the distribution of performance indices(associated with sensor data from a space) over the various performanceindicators. Each tier of the plot may represent one of the thresholdbins. Each point in the plot may represent a rolling average of cleanedconcentration data of one IAQ parameter. Rolling averages may becalculated over any suitable time period, including for example, 1-hour,8-hour, 24-hours, and the like. For example, the point may represent a1-hour or 8-hour rolling average of cleaned concentration data of oneIAQ parameter during typical occupied hours. Percentages to the right ofthe plot may represent the percent of cleaned concentration data duringtypical occupied hours that fall in each threshold bin. As shown in FIG.5 , in the left most plot, an overlay across all the parameters may bevisualized. Alternatively or additionally, summary plots for each IAQparameter may be determined.

In some embodiments, the reports shown in FIG. 5 may be generated foreach floor of a commercial space in which the described systems andmethods are implemented. For example, FIG. 6 provides an example of anIAQ summary plot generated across a plurality of different floors in acommercial building. In some embodiments, the report may provide asummary of sensors, or floors that require an alert or alarm. As shown,a summary across all parameters for Floor one may be provided in theleft-most plot of FIG. 6 . Additionally, the graphical interface mayprovide a summary by sensor as shown in the right-side plots of FIG. 6 .For example, a graphical representation of the overall score such as abadge can be displayed. In some embodiments, the graphicalrepresentation can be generated for each floor as well as for eachsensor. In some embodiments, the badges may display text such as “HealthOptimized” or “Excellent” and be color-coded when displayed to a user.

In some embodiments, the graphical user interface can be used toidentify sensors that indicate IAQ issues. For example, a user may firstbe presented with data corresponding to all parameters such as was shownin FIG. 5 . A user may then select a particular parameter (i.e., TVOC)for further review, and be presented with a visualization correspondingto the left-panel of FIG. 6 . The user may then select a particularsensor affiliated with the selected parameter (i.e., sensors 11-15) forfurther review, as illustrated in the right-panel of FIG. 6 .

FIG. 7 provides an example of an IAQ map 700, which provides a graphicalrepresentation of the sensor data for a single parameter as compared tothresholds for a whole building over a time period. As shown, data foreach date in the time (x-axis) can be plotted for each sensor (y-axis).Each data point in the IAQ map 700 can be assigned a different color,icon, or the like, based on the threshold range where the data pointfalls. Accordingly, the IAQ map 700 may provide a summary of data fromall sensors for all time (i.e., typically occupied and unoccupied hours)in a building for a single IAQ parameter over the wholetime period forwhich a visual is generated. Each point may represent a rolling averageconcentration of the parameter shown. The IAQ map 700 may provide a userwith a visual representation in trends in the data over space and time,allowing a user to quickly located sensors that may warrantinvestigation. IAQ maps can be generated for any parameter, includingbut not limited to, CO₂, PM_(2.5), TVOC, noise, and the like. In someembodiments, the time period for which summary data is provided may beprovided by a user, and may correspond to an entire reporting time,occupied hours only, unoccupied hours only, or the like.

FIG. 8 provides an example of an IAQ map 800 of 1-hour rolling averageCO2 concentrations, which provides a graphical representation of CO₂₁-hour rolling averages over the reporting period. As shown, in animplementation where the systems and methods described herein weredeployed in a commercial building, the IAQ map 800 may show CO₂ ₁-hourrolling averages in the Health optimized, Excellent, Action, Alert, andLimit ranges for a subset of sensors.

FIG. 9 provides an example of an IAQ map 900 of 1-hour rolling averagePM_(2.5) concentrations, which provides a graphical representation ofPM_(2.5) in 1-hour rolling averages over the reporting period.Accordingly, a user may identify a sensor at issue based on the visualrepresentation and perform investigations into what the sensordetermined as action or alert levels.

FIG. 10 provides an example of an IAQ map 1000 of 1-hour Rolling AverageTVOC concentrations, which provides a graphical representation of TVOCin 1-hour rolling averages over the reporting period.

FIG. 11 provides an example of a visualization for a timeseries 1100 inaccordance with some embodiments of the present disclosure. Asillustrated in FIG. 11 , data for a particular set of sensorscorresponding to a parameter may be plotted on the y-axis across aplurality of dates on the x-axis. The concentration for a specificparameter plotted on the y-axis may be scaled based on the maximumconcentration of that parameter in the data. Measurements from eachsensor for the same IAQ parameter may be plotted. A graphicalrepresentation of cleaned raw data for all time may be displayed underlines that demonstrate rolling averages of the cleaned raw data. Bandsidentifying limit, action, and alert ranges may be overlaid upon thedata. The timeseries visualization 1100 may convey patterns inmeasurements of an individual parameter by all sensors on a single floorover the reporting period. In particular, FIG. 11 provides avisualization for a timeseries 1100 of TVOC over time, and provides agraphical illustration of when parameter data is determined to be abovecertain thresholds.

FIG. 12 provides an example of a visualization for a timeseries 1200 inaccordance with some embodiments of the present disclosure. Asillustrated in FIG. 12 , a report may be generated illustrating thetimeseries visualization for each parameter for each floor. As shown, insome embodiments, timeseries data can be shown for each parameterseparately in separate subplots within a graphical user interface. Eachsubplot may also illustrate each sensor separately. Accordingly, reportsmay be generated for each floor. For example, timeseries may begenerated for temperature, RH, or noise from all sensors on a singlefloor over the reporting period. The timeseries plots can be used toidentify sensors of interest.

FIG. 13 provides an example of a visualization for temperature andrelative humidity 1300. As illustrated in FIG. 13 , a summary plot maybe used to convey how measured temperature and RH may compare toexposure thresholds during the reporting period. As shown, a rollingaverage of temperature measurements may be plotted on the x-axis againsta rolling average of relative humidity. The location of each data pointmay represent the rolling average temperature and relative humidity at asingle point in the time during the occupied hours of the building. Insome embodiments, points may be shaded or colored to represent therelationship between the data and the thresholds. In some embodiments,temperature and RH are scored together. For example, a temperature of 72F and an RH of 40% would receive a score of Health Optimized, while atemperature of 72 F and an RH of 62% would receive a score of Excellent.Relative humidity is a measure of the amount of water vapor present inair expressed as a percentage of the amount needed for saturation at thesame temperature. RH_(out−x), where x=1, 6, or 11, is defined as xpercentage points less than the RH that a parcel of outdoor air wouldhave if its temperature was changed from current outdoor temperature tothe current indoor temperature without changing the water content of theair. In some embodiments, when rolling averages fall in the HealthOptimized and Excellent ranges, any negative impacts of thermalconditions on occupant health, productivity, and comfort are expected tobe minimized. If rolling averages consistently fall in the Action range,which is not uncommon in some office buildings, the monitored area wherethe exceedances occurred may be evaluated for potential sources ofsuboptimal thermal conditions. When rolling averages consistently fallin the Alert and Limit ranges, the monitored area where the exceedancesoccurred should be evaluated and corrective actions should beidentified. A graphical illustration for displaying the distribution ofdata for two parameters obtained from sensors in the building can bebuilt for any suitable spatial scale including by floor, building, setof buildings and the like.

FIG. 14 provides an example of a visualization for temperature andrelative humidity 1400 across a plurality of floors in a commercialbuilding. As shown, visualizations may be generated for each floor. Insome embodiments, additional visualization elements such as badges thatvisually represent scores can be shown for each sensor and/or floor.

FIG. 15 provides an example of a visualization for noise data in a noisemap 1500. As illustrated in FIG. 15 , a noise map 1500 may be used toconvey how data points related to noise compared to thresholds over thereporting period. For example, the date may be plotted along the x-axisand the sensor ID may be plotted along the y-axis. The noise map 1500may provide a summary for cleaned data from all the sensors for theentire reporting period, including the hours where the building isoccupied or unoccupied. Each point may represent a rolling average ofnoise measurements, and the shape and/or color of each point maycorrespond to the threshold range where the average falls. The noisedata map 1500 may provide a user with the ability to evaluate trends indata over space and time. Although a noise map is shown, similarvisualizations for other parameters may be generated.

FIG. 16 provides an example of a visualization for a noise boxplot 1600.As shown, the noise boxplot 1600 may provide an indication of how noisemeasurements from a particular sensor compared to thresholds during thereporting period. The left side of the box may indicate the 25^(th)percentile of 1-hour rolling averages of noise measurements, the middleline in the box shows median of 1-hour rolling averages of noisemeasurements, and the right side of the box shows 75^(th) percentile of1-hour rolling averages of noise measurements. The noise boxplot 1600may also include bands to indicate the corresponding thresholds. Thenoise boxplot 1600 may include plotting rolling average of noisemeasurements (dBA) on the x-axis. Although noise measurements for aparticular sensor are illustrated in FIG. 16 , it is envisioned thatsimilar noise boxplots can be developed for a whole portfolio ofbuildings, a single building's noise data, and/or a whole floor's noisedata.

FIG. 17 provides an example of a visualization for the noise boxplot1700. As shown, in some user interface a first portion of the graphicaluser interface may be configured to include a summary noise boxplot. Asecond portion of the graphical user interface can be configured toillustrate component parts of the data set contributing to the summarynoise boxplot. For example, a noise boxplot for each floor and/or eachsensor may be visualized separately in a second portion of the graphicaluser interface, as shown on the right panel of FIG. 17 . In someembodiments, additional visualization elements such as badges thatvisually represent scores can be shown for each sensor and/or floor.

FIG. 18 provides an example visualization for data quality checks. Asshown, in some embodiments data quality checks may include timeseriesplots of data that is outside of measurement ranges 1800. For example,data from sensors with more than 1% of data outside the sensormeasurement ranges in the reporting period may be represented visually.

FIG. 19 provides a second example visualization for data quality checks.As shown, in some embodiments, data quality checks may include plots ofsensors with abnormally low variation 1900. This plot 1900 flags sensorsthat may have issues with abnormally low variation, where individualparameters only took on one or two unique values.

FIG. 20 illustrates an exemplary graphical user interface used fordetecting sensor issues. For example, the IAQ map can be used todetermine that a particular sensor (i.e., sensor 21) consistently sensesdata in the set category (i.e., Limit) for the duration of the entiretime period.

FIG. 21 illustrates an exemplary graphical user interface used fordetecting a floor issue. For example, the IAQ map can be used todetermine that a particular floor underwent an event as shown by all ofthe sensors associated with a particular floor (e.g., Sensors 21-25,inclusive) sensing data belonging to elevated categories (i.e., Limit)for the same time period.

FIG. 22 illustrates an exemplary graphical user interface used fordetecting a building issue. For example, the IAQ map can be used todetermine that a building underwent an event as shown by all of thesensors associated with a particular building sensing data belonging toelevated categories (i.e., alert) for the same time period (e.g.,weekdays in January in the afternoons.).

Other embodiments are within the scope and spirit of the disclosedsubject matter. For example, the monitoring system described in thisapplication can be used in facilities that have complex machines withmultiple operational parameters that need to be altered to change theperformance of the machines (e.g., building automation systems). Usageof the word “optimize”/“optimizing” in this application can imply“improve”/“improving.”

Certain exemplary embodiments are described herein to provide an overallunderstanding of the principles of the structure, function, manufacture,and use of the systems, devices, and methods disclosed herein. One ormore examples of these embodiments are illustrated in the accompanyingdrawings. Those skilled in the art will understand that the systems,devices, and methods specifically described herein and illustrated inthe accompanying drawings are non-limiting exemplary embodiments andthat the scope of the present invention is defined solely by the claims.The features illustrated or described in connection with one exemplaryembodiment may be combined with the features of other embodiments. Suchmodifications and variations are intended to be included within thescope of the present invention. Further, in the present disclosure,like-named components of the embodiments generally have similarfeatures, and thus within a particular embodiment each feature of eachlike-named component is not necessarily fully elaborated upon.

The subject matter described herein can be implemented in digitalelectronic circuitry, or in computer software, firmware, or hardware,including the structural means disclosed in this specification andstructural equivalents thereof, or in combinations of them. The subjectmatter described herein can be implemented as one or more computerprogram products, such as one or more computer programs tangiblyembodied in an information carrier (e.g., in a machine-readable storagedevice), or embodied in a propagated signal, for execution by, or tocontrol the operation of, data processing apparatus (e.g., aprogrammable processor, a computer, or multiple computers). A computerprogram (also known as a program, software, software application, orcode) can be written in any form of programming language, includingcompiled or interpreted languages, and it can be deployed in any form,including as a stand-alone program or as a module, component,subroutine, or other unit suitable for use in a computing environment. Acomputer program does not necessarily correspond to a file. A programcan be stored in a portion of a file that holds other programs or data,in a single file dedicated to the program in question, or in multiplecoordinated files (e.g., files that store one or more modules,sub-programs, or portions of code). A computer program can be deployedto be executed on one computer or on multiple computers at one site ordistributed across multiple sites and interconnected by a communicationnetwork. An algorithm can include a computer program. An algorithm caninclude computer executable instructions (e.g. that can be executed by aprocessor).

The processes and logic flows described in this specification, includingthe method steps of the subject matter described herein, can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions of the subject matter describedherein by operating on input data and generating output. The processesand logic flows can also be performed by, and apparatus of the subjectmatter described herein can be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application-specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processor of any kind of digital computer. Generally, aprocessor will receive instructions and data from a Read-Only Memory ora Random Access Memory or both. The essential elements of a computer area processor for executing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, e.g.,magnetic, magneto-optical disks, or optical disks. Information carrierssuitable for embodying computer program instructions and data includeall forms of non-volatile memory, including by way of examplesemiconductor memory devices, (e.g., EPROM, EEPROM, and flash memorydevices); magnetic disks, (e.g., internal hard disks or removabledisks); magneto-optical disks; and optical disks (e.g., CD and DVDdisks). The processor and the memory can be supplemented by, orincorporated in, special purpose logic circuitry.

To provide for interaction with a user, the subject matter describedherein can be implemented on a computer having a display device, e.g., aCRT (cathode ray tube) or LCD (liquid crystal display) monitor or phone,for displaying information to the user and a keyboard and a pointingdevice, (e.g., a mouse or a trackball), by which the user can provideinput to the computer. Other kinds of devices can be used to provide forinteraction with a user as well. For example, feedback provided to theuser can be any form of sensory feedback, (e.g., visual feedback,auditory feedback, or tactile feedback), and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The techniques described herein can be implemented using one or moremodules. As used herein, the term “module” refers to computing software,firmware, hardware, and/or various combinations thereof. At a minimum,however, modules are not to be interpreted as software that is notimplemented on hardware, firmware, or recorded on a non-transitoryprocessor readable recordable storage medium (i.e., modules are notsoftware per se). Indeed “module” is to be interpreted to always includeat least some physical, non-transitory hardware such as a part of aprocessor or computer. Two different modules can share the same physicalhardware (e.g., two different modules can use the same processor andnetwork interface). The modules described herein can be combined,integrated, separated, and/or duplicated to support variousapplications. Also, a function described herein as being performed at aparticular module can be performed at one or more other modules and/orby one or more other devices instead of or in addition to the functionperformed at the particular module. Further, the modules can beimplemented across multiple devices and/or other components local orremote to one another. Additionally, the modules can be moved from onedevice and added to another device, and/or can be included in bothdevices.

The subject matter described herein can be implemented in a computingsystem that includes a back-end component (e.g., a data server), amiddleware component (e.g., an application server), or a front-endcomponent (e.g., a client computer having a graphical user interface ora web interface through which a user can interact with an implementationof the subject matter described herein), or any combination of suchback-end, middleware, and front-end components. The components of thesystem can be interconnected by any form or medium of digital datacommunication, e.g., a communication network. Examples of communicationnetworks include a local area network (“LAN”) and a wide area network(“WAN”), e.g., the Internet.

Approximating language, as used herein throughout the specification andclaims, may be applied to modify any quantitative representation thatcould permissibly vary without resulting in a change in the basicfunction to which it is related. Accordingly, a value modified by a termor terms, such as “about” and “substantially,” are not to be limited tothe precise value specified. In at least some instances, theapproximating language may correspond to the precision of an instrumentfor measuring the value. Here and throughout the specification andclaims, range limitations may be combined and/or interchanged, suchranges are identified and include all the sub-ranges contained thereinunless context or language indicates otherwise.

What is claimed is:
 1. A method comprising: receiving datacharacterizing a time-dependent first sensor data detected by a firstsensor, a time-dependent second sensor data detected by a second sensor,a set of first threshold values associated with the first sensor, a setof second threshold values associated with the second sensor and a timewindow, wherein the first sensor and the second sensor are located in afirst space of a building; calculating a first performance index basedon the first sensor data and the time window and a second performanceindex based on the second sensor data and the time window; classifyingthe first performance index and the second performance index into one ofa plurality of performance indicators wherein the classification of thefirst performance index and the second performance index is based oncomparison of the first performance index and the second performanceindex with the first set of threshold values and the second set ofthreshold values, respectively; determining a performance rating scorefor the first space by scoring the classification of first performanceindex and the second performance index within the plurality ofperformance indicators; and providing the performance rating scoreassigned to the first space.
 2. The method of claim 1, whereincalculating the first performance index includes: selecting a firstportion of the time-dependent first sensor data that temporally spansfrom a first time to a second time, wherein the difference between thesecond time and the first time corresponds to the time window; andcalculating the first performance index by averaging the first portionof the time-dependent first sensor data.
 3. The method of claim 2,further comprising calculating a third performance index, thecalculating includes: selecting a second portion of the time-dependentfirst sensor data that temporally spans from a third time to a fourthtime, wherein the difference between the fourth time and the third timecorresponds to the time window; and calculating the third performanceindex by averaging the second portion of the time-dependent first sensordata.
 4. The method of claim 3, further comprising: classifying thefirst performance index to a first category of the plurality ofcategories; and classifying the third performance index to a secondcategory of the plurality of categories, wherein the first performanceindex is greater than the first threshold value associated with thefirst sensor, and the second performance index is smaller than the firstthreshold value.
 5. The method of claim 4, further comprising:rendering, in a graphical user interface, a first visual representationof the first category of the plurality of categories and a second visualrepresentation of the second category of the plurality of categories;generating a first graphical object indicative of the first performanceindex; generating a second graphical object indicative of the thirdperformance index; and rendering, in the graphical user interface, thefirst graphical object over the first visual representation and thesecond graphical object over the second visual representation.
 6. Themethod of claim 5, wherein the first graphical object is rendered in afirst region of the graphical user interface at a first time, whereinthe first graphical object traverses from the first region of thegraphical region to the first visual representation during a time periodsubsequent to the first time.
 7. The method of claim 1, furthercomprising: rendering, in a graphical user interface, a visualrepresentation of at least one of the first performance indicator, thesecond performance indicator, and the performance rating score of thefirst space for a first time period; and rendering in a graphical userinterface a second visual representation of at least one of the firstperformance indicator, the second performance indicator, and theperformance rating score of the first space for a second time period. 8.The method of claim 1, further comprising: receiving environmental dataincluding at least one of ventilation, infiltration, recirculationrates, heating filter type, airflow, space dimensions, and floor plans;and determining a first sensor position for a first sensor and a secondsensor position for a second sensor within the first space of thebuilding.
 9. The method of claim 1 further comprising: receiving datacharacterizing a time-dependent third sensor data detected by a thirdsensor, and a set of third threshold values associated with the thirdsensor, wherein the third sensor is located in the first space of thebuilding; calculating a third performance index based on the thirdsensor data and the time window; classifying the third performance indexinto one of a plurality of performance indicators wherein theclassification of the third performance index is based on a comparisonof the third performance index with the third set of threshold values;and wherein determining the performance rating score for the first spacefurther comprises scoring the classification of the third performanceindex within the plurality of performance indicators.
 10. The method ofclaim 9, wherein the set of third threshold values associated with thethird sensor corresponds to noise.
 11. A system comprising: at least onedata processor; memory coupled to the at least one data processor, thememory storing instructions to cause the at least one data processor toperform operations comprising: receiving data characterizing atime-dependent first sensor data detected by a first sensor, atime-dependent second sensor data detected by a second sensor, a firstset of threshold values associated with the first sensor, a second setof threshold values associated with the second sensor and a time window,wherein the first sensor and the second sensor are located in a firstspace of a building; calculating a first performance index based on thefirst sensor data and the time window and a second performance indexbased on the second sensor data and the time window; classifying thefirst performance index and the second performance index into one of aplurality of performance indicators wherein the classification of thefirst performance index and the second performance index is based oncomparison of the first performance index and the second performanceindex with the first set of threshold values and the second set ofthreshold values, respectively; determining a performance rating scorefor the first space by scoring the classification of first performanceindex and the second performance index within the plurality ofperformance indicators; and providing the performance indicator assignedto the first space.
 12. A computer program product comprising anon-transitory machine-readable medium storing instructions that, whenexecuted by at least one programmable processor that comprises at leastone physical core and a plurality of logical cores, cause the at leastone programmable processor to perform operations comprising: receivingdata characterizing a time-dependent first sensor data detected by afirst sensor, a time-dependent second sensor data detected by a secondsensor, a first threshold value associated with the first sensor, asecond threshold value associated with the second sensor and a timewindow, wherein the first sensor and the second sensor are located in afirst space of a building; calculating a first performance index basedon the first sensor data and the time window and a second performanceindex based on the second sensor data and the time window; classifyingthe first performance index and the second performance index into one ofa plurality of performance indicators wherein the classification of thefirst performance index and the second performance index is based oncomparison of the first performance index and the second performanceindex with the first threshold value and the second threshold value,respectively; determining a performance rating score for the first spaceby scoring the classification of first performance index and the secondperformance index within the plurality of performance indicators; andproviding the performance indicator assigned to the first space.